Tag: growth signals (Page 1 of 2)

Hydrogen is hiring: what the PredictLeads Jobs dataset says about sector health in 2025

If you want to know whether a sector is actually moving, don’t start with hype – start with hiring. We used the PredictLeads Jobs dataset (last 3 months) across leading hydrogen names to “nowcast” sector health. The takeaway: deployment is real, and it shows up in job titles first.

TL;DR

  • The PredictLeads Jobs dataset shows strong and recent hiring activity at major hydrogen companies, particularly in roles connected to deployment such as field and service positions, manufacturing, and engineering.
  • External market signals are consistent with what the hiring data reveals. The Global X Hydrogen Exchange Traded Fund (ticker symbol HYDR) has risen in 2025, reflecting investor optimism in the hydrogen sector. The International Energy Agency reports that hydrogen demand continues to grow and that there has been a wave of projects reaching the stage of Final Investment Decision, where companies formally commit capital to build. In parallel, the European Union Hydrogen Bank is providing funding for additional renewable hydrogen production capacity.
  • Falling interest rates are providing a supportive backdrop for capital-expenditure-intensive technologies such as hydrogen. The European Central Bank reduced its benchmark interest rate by 25 basis points in both March 2025 and April 2025, and the United States Federal Reserve lowered its policy rate in September 2025.

From job ads to energy shifts: What hiring tells us about the future of hydrogen.

What the PredictLeads Jobs dataset shows (last 3 months)

Air Liquide, Bloom Energy, and Plug Power are the backbone of current hiring:

  • Air Liquide: Fresh postings spike into September – a classic “projects greenlit → staff up” seasonality you expect when deployments move.
  • Bloom Energy: Steady month-over-month momentum. Stack R&D + manufacturing roles show factories and product lines scaling.
  • Plug Power: Heavy field & service footprint (commissioning, technicians, sustaining). That’s boots-on-the-ground work (aka real deployments).

Across companies, the role mix skews toward:

  • Field & Service → signal of installs, commissioning, and uptime SLAs.
  • Manufacturing → signal of throughput and factory capacity.
  • R&D & Engineering → ongoing stack, electrolyzer, and balance-of-plant improvements.

Why this matters: when a sector shifts from “talk” to “deploy,” job titles change first. The PredictLeads Jobs dataset is the fastest way to catch that turning point.


External confirmation the sector is moving (beyond our dataset)

Market proxy — HYDR ETF. The Global X Hydrogen ETF is up in 2025 on common trackers. That doesn’t prove revenues company-by-company, but it’s a clean risk sentiment read that aligns with our hiring picture.

IEA’s 2025 view – The IEA Global Hydrogen Review 2025 reports demand rising to ~100 Mt in 2024 and highlights 200+ FIDs through end-2024, i.e., a pipeline that naturally pulls hiring in engineering, manufacturing, and service. Growth is uneven, but the trajectory and investment signals are there. (FID being Final Investment Decisions)

EU Hydrogen Bank funding. The second auction drew strong interest and awarded ~€1 billion to 15 projects across the EU – another “real money → real people” link that matches the roles we see in the Jobs dataset.


Why rate cuts matter (and help what we’re seeing in the jobs data)

Hydrogen projects are capital-intensive. Lower rates improve project IRRs and make financing/offtake less painful. In 2025:

  • ECB reduced interest rates by 25 basis points in March and again in April which shows support for EU project finance.
  • Fed delivered its first 2025 cut in September – a broader risk-on nudge that tends to help thematics like H₂.

How to use the PredictLeads Jobs dataset like a pro

Steal this mini-playbook:

  1. Nowcast sector health
    Build a simple monthly postings index for a curated “Hydrogen 20” basket. Watch the mix shift from R&D → Field/Service/Manufacturing to know when deployments ramp.
  2. Commissioning heatmap
    Filter titles for “field”, “service”, “commissioning”. Map locations to see where projects are turning on. Use it for partner targeting and on-the-ground ops.
  3. Capacity & supply chain
    Track manufacturing roles (operators, line leads, welders). That’s your proxy for throughput and vendor demand coming down the chain.
  4. Talent & wage checks
    When ranges are present, parse & annualize to benchmark pay (useful for staffing, contractors, and budgeting).
  5. Bridge to markets (optional)
    Overlay your postings index with HYDR monthly returns and test 0–3-month lags. Hiring responds slower than prices, but the direction should rhyme if you’ve got the basket right. (The widget above lets you keep an eye on HYDR in real time.)

Bottom line

Hiring is one of the cleanest early signal we have. In hydrogen, the PredictLeads Jobs dataset shows the shift from “talk” to deploy: more field/service, more manufacturing, steady engineering. That’s what real projects look like from the inside.


Who we are (and why this works)

PredictLeads is a data provider focused on commercial signals (Jobs, News, Technologies, and more) delivered via API, FlatFiles and webhooks so you can plug insight directly into your models, decks, or ops. No platform to learn, just the data you need.

If you’re exploring hydrogen (or any sector where deployment beats hype) use the PredictLeads Jobs dataset as your lead signal.
Docs: https://docs.predictleads.com/v3

The Billion-Dollar Clues Hiding in The Right Blend of Company Data

In 2012, Stripe was just a little payments API that almost nobody outside of Silicon Valley had heard of.
By 2021, it was worth $95 billion.

The uncomfortable truth is the signals that Stripe was going to be huge were visible years before the big headlines hit. Most people just weren’t looking for that crucial early-stage investment signals (or didn’t know where to look).

That’s the edge today’s smartest investors are chasing: finding billion-dollar companies before they look like billion-dollar companies. And it starts with something almost no one talks about. The right blend of News and Connections data.

The Secret’s in the Signals

At PredictLeads, we monitor more than 20 million news sources and close to 100 million companies worldwide, capturing early-stage investment signals in a company’s journey. Spaning from funding rounds and product launches to strategic partnerships, hiring surges, and market expansions.

But we don’t stop at just the news.

Our Connections dataset maps the business relationships that reveal how a company is truly positioning itself in the market – from product integrations and investor ties to vendor agreements and partnerships with industry leaders. This is done by scaning company websites for partner and customer logos, using our image recognition system to match each logo to a verified domain. We also analyze case study pages, testimonials, and “Our Customers” sections to uncover customers, partners, vendors, and investors that often go unreported in press releases or traditional news.

Each connection is a signal of strategic intent: integrations hint at ecosystem alignment, investor relationships point to future funding potential, and vendor or partner deals often precede market entry or expansion. When combined with our other datasets, these connections turn scattered updates into a clear, data-backed narrative of growth — and within that narrative is where the next unicorn often emerges.

The Pattern Every Investor Dreams Of

Picture this:
January > a startup raises a modest $8M Series A.
February > they integrate with Stripe’s API.
March > our company data shows a vendor relationship with Shopify.
April > they expand into London and start hiring engineers at double the previous rate.

If you’re only reading headlines, you’ll miss the story.
If you’re tracking news events and company connections in real time, you’ll see it months before the rest of the market and you’ll be in the room when the deal is still hot.

Why Public Headlines Are Too Late

By the time TechCrunch reports a $100M Series C, the race is already crowded and you’re not ahead of the game, you’re simply keeping pace with everyone else.

To spot opportunities earlier, you need to look where others aren’t. News data reveals unannounced or smaller funding rounds — early startup investment signals that indicates momentum gain. Connections data uncovers the strategic moves behind that momentum, from product integrations and new partnerships to key customer wins and vendor relationships.

Overlay these signals, and you will not wait for the news — you’ll see them coming. The result is an early warning system for hypergrowth, giving you a competitive edge long before the headlines hit.

The Future of Investment Intelligence

In the next five years, the biggest wins in venture won’t go to the investors with the most meetings — they’ll go to the ones who can see conviction in the data before the rest of the market believes it.

The edge won’t come from chasing every funding headline, but from quietly tracking the early indicators of momentum: a new integration with a market leader, a sudden hiring surge in engineering, an unexpected expansion into a high-growth region.

When you can spot these early-stage investment signals as they happen — and connect them into a bigger story — you stop reacting to the market and start anticipating it. Finding the next unicorn and its startup investment signals isn’t about luck; it’s about reading the signals early enough to act, while the opportunity is still invisible to everyone else.

If you’re ready to see what those whispers sound like, let’s talk.

What Summer BBQs Can Teach Us About Reading B2B Buying Signals

It’s a Saturday in mid-July and you’ve been invited to four different BBQs.

You’re walking through a quiet suburban neighborhood, sunglasses on, sandals flapping. The sun is relentless, the scent of grilled meat hangs in the air… and you’re on a mission. 🥩🧑‍🍳

The first house?
You catch a whiff of burnt tofu and hear someone ask if the kombucha is homemade.

Hard pass.

You keep moving.

A few steps down, you hear music (real music) and spot a lineup of Ford Raptors and a 96 Chefy parked out front. There’s laughter behind a wooden fence, and you catch sight of a green ceramic grill puffing steady smoke, with a line forming around the buffet table.

You don’t need to ask for a menu.
You already know:

This is the one worth joining.

You skip the silent lawns and low-energy gatherings and you:
1. Read the signals.
2. Follow the smoke.
3. Choose wisely.

🎯 In B2B Sales and Investing, the Same Rules Apply

Some companies signal quality before you even step in the door.
Their websites, partners, and public presence give off subtle (and measurable) signs:

  • Logos of well-known brands appear on their sites.
  • Integrations and partnerships get highlighted.
  • Case studies and testimonials drop recognizable names.
  • All of it is smoke – but in this case, smoke that matters.

It’s all smoke! But in this case – it means something.

In B2B such smoke isn’t always obvious. That’s why we built the Connections Dataset at PredictLeads – to read the grill smoke signals at scale.

🔍 Why Logos Matter and Why They’re Hard to Track

To gain credibility, B2B startups often put logos of companies they work with directly on their websites. These show up under sections like:

  • “Our Customers”
  • “Trusted by”
  • “Partners”
  • “Who we work with”
  • Testimonials or Case Study pages

The challenge?
Most of these logos are not backlinked. There’s no easy text trail or hyperlink to follow. A Google search won’t help. Scraping doesn’t cut it.

So we built something smarter.

Logo Recognition Meets Entity Mapping

Our system uses image recognition to detect logos on company websites. Then we match those logos to verified domain names and legal entities.

This enables us to connect:

  • Which company is claiming a relationship
  • Who the other party is (vendor, partner, customer, etc.)
  • Where and how that connection is represented

We don’t just scan the homepage. We parse through case study sections, customer lists, footers, header navs, press pages (anywhere companies hint at collaboration).

Each relationship is then categorized:

  • “vendor” → “Company A is a vendor to Company B”
  • “partner” → “Company A collaborates with Company B”
  • “integration” → “Company A integrates with Company B”
  • “investor”, “published_in”, “parent”, “rebranding” (and more)

We even timestamp when we first and last saw the connection. That means you can prioritize based on recency and relationship type.

🧾 Example: Invoicy → Salesforce

Let’s say a small fintech startup called Invoicy includes a line on their “Customers” page that says:

“Trusted by finance teams at companies like Salesforce, Rippling, and Brex.”

There are no backlinks. Just static logos and a sentence tucked beneath a testimonial.

Our system scans the page, detects the Salesforce logo, maps it to the domain salesforce.com, and parses the surrounding text.

The language >“trusted by finance teams”< suggests that Invoicy is a vendor to Salesforce, likely providing tooling for invoicing, reconciliation, or internal financial workflows.

That gets recorded as:

  • category: “vendor”
  • source_url: the exact URL of the “Customers” page
  • first_seen_at: when the connection was first detected
  • last_seen_at: when it was last confirmed

For a company like Invoicy, being able to show they’re used by a giant like Salesforce is a huge trust signal and even more so when made searchable and machine-readable.

Now sales teams, investors, and analysts can factor that credibility directly into targeting models, scoring frameworks, or due diligence … without ever scraping a webpage by hand.

🔥 What This Means for You

For GTM teams:
Use vendor and partner relationships to qualify and prioritize leads.
If your ICP already sells to Snowflake, Notion, or Google – that’s your BBQ. Bring your best pitch.

For investors:
Track which startups are gaining traction with known buyers.
Logos and partnerships are sometimes more honest than press releases.

For growth teams:
Score accounts based on who trusts them.
If they’ve passed another company’s procurement process, they’re likely enterprise-ready.

🛠️ The Grill is Hot so Start Reading the Signals!

You wouldn’t walk into a BBQ blind. You look for smoke, listen for music, and trust the signs.

The same goes for B2B:

Who they work with tells you who they are.

And PredictLeads helps you see that across millions of companies in real time.

Want a quick walkthrough or test run of the Connections Dataset?
Explore the PredictLeads API

How AI Sales Agents Are Transforming B2B Prospecting and How PredictLeads Steps In

Over the last 18 months, AI agents have gone from experimental prototypes to everyday tools transforming how go-to-market (GTM) teams work. The emergence of AI sales agents has revolutionized traditional methods. Today, AI sales agents can automate lead qualification, personalize outreach, prioritize accounts, and enrich CRMs — at a scale humans simply can’t match.

But here’s the catch: AI is only as good as the data you feed it.
Even the most advanced agent can’t create meaningful output without real-time, event-based company intelligence. AI sales agents benefit greatly from data-driven insights, and that’s exactly where PredictLeads comes in.


What Is PredictLeads?

PredictLeads is a data provider built for modern GTM, sales, marketing, and investment teams. Our infrastructure tracks 92M+ companies globally and provides dynamic signals that go far beyond static firmographics, crucial for AI sales agents.

We capture:

Instead of manually compiling lists, you can plug into our API or webhooks to enrich leads, monitor accounts, and score opportunities in real-time. This is where AI sales agents truly shine.


Why AI Agents Need Event-Based Company Data

Here’s the truth: most AI agents are bottlenecked by poor context.

Whether you’re building in LangChain, AutoGPT, OpenAgents, Pipedream, n8n, or Zapier, many agents still rely on outdated CRMs or static CSVs. That means they lack the situational awareness needed to act intelligently. AI sales agents that have access to real-time data perform best.

PredictLeads changes that. By feeding your AI with real-time hiring, funding, technology, and partnership signals, you create agents that don’t just automate tasks — they anticipate market shifts.


Example: An AI SDR Agent

Imagine this workflow:

  1. AI monitors 10,000 target accounts.
  2. Detects when a company hires a Sales Enablement Manager or adopts Outreach.io.
  3. Generates a personalized intro email mentioning the hiring signal and tech stack.
  4. Pushes the draft to an SDR’s inbox or LinkedIn sequence.

This isn’t theoretical. Teams are already building these automations with PredictLeads + AI agents, exemplifying the true potential of AI sales agents.


Top Use Cases for PredictLeads in AI Workflowsads

Use CaseDatasetAI Output
Outbound AutomationJob Openings + TechnologiesPersonalized emails or LinkedIn messages
Account ScoringNews Events + FundingDynamic ICP fit scoring
CRM EnrichmentCompanies + Website EvolutionAuto-filled account descriptions & tags
Market MappingConnections + Tech DetectionsRelationship graphs and industry maps
Timing SignalsJob ads + Product LaunchesPredictive lead routing and prioritization

Built for AI-First AI Sales Agents Workflows

Our API-first architecture gives AI agents exactly what they need:

  • JSON responses and simple endpoints
  • Daily refreshed datasets
  • Filters by title, tech, domain, industry, revenue, geography
  • Works seamlessly in Pipedream, n8n, Make.com, Zapier, Retool, Hex, or your data warehouse

No login UI. No bloated dashboards. Just raw, real-time signals delivered at scale — the way AI expects them.


Why This Matters in 2025

AI sales agents are getting smarter and more autonomous every month. But autonomy without context is just automation.

By pairing AI sales agents with PredictLeads’ event-based company intelligence, GTM teams gain:

  • Faster awareness of shifts in buyer behavior
  • Sharper targeting based on real-world company events
  • Smarter automation that adapts as markets move

The future isn’t about replacing sales teams with bots. It’s about enabling them with AI sales agents that understand companies as they evolve.


Final Thoughts

At PredictLeads, we believe the next wave of GTM efficiency will come from AI sales agents powered by live market signals.

If you’re building AI tools that need to know what companies are doing — not just who they are — we should talk.

Using PredictLeads + Polytomic to Power GTM Execution (in HubSpot and Salesforce)

Modern go-to-market teams rely on timely data to prioritize accounts, launch targeted campaigns, and coordinate sales and marketing outreach. Yet too often, valuable buying signals get buried in spreadsheets or trapped in data warehouses out of reach for the teams who need them most.

That’s why we’re excited to share how teams can now use Polytomic to ingest PredictLeads data and sync it directly into CRMs like HubSpot and Salesforce which enables faster, more data-driven GTM execution.

Why is this worth checking out? 

PredictLeads provides structured datasets that reveal what companies are doing today and not just who they are. One of the most actionable sources is the Jobs dataset, which includes job openings published by companies across regions, industries, and roles.

This data becomes even more valuable when combined with Polytomic’s no-code integration and sync capabilities. Companies can now ingest and filter PredictLeads datasets inside Polytomic and push enriched company profiles directly into downstream systems such as Salesforce or HubSpot.

The result? GTM teams can identify the right accounts earlier and take action faster + without waiting for engineering teams to build pipelines or sync logic (read – lower cost overall).

Some Examples

Below are specific ways companies are already leveraging PredictLeads + Polytomic to accelerate sales and marketing efforts:

1. Identify Companies Expanding Their Marketing Teams

A B2B marketing automation company can use PredictLeads to track companies hiring for roles like “Head of Demand Generation” or “Growth Marketing Manager” across North America.

Using Polytomic, they can filter the dataset to include only companies hiring in target regions or industries and sync those records to Salesforce with enriched fields like job title, location, and department.

This gives SDRs a live list of companies expanding marketing efforts which often leads to indicators of new technology investment.

2. Prioritize Sales Outreach Based on Engineering Hires

A DevOps platform provider can monitor companies hiring for “DevOps Engineers” or “Platform Engineers.”

When PredictLeads detects these job openings, Polytomic can automatically add these companies to a HubSpot static list, assign them to specific reps, or trigger sequences.

This ensures the sales team is focusing on companies building out the exact functions their product supports.

3. Regional Expansion Tracking

A SaaS company entering the DACH market can use PredictLeads to identify existing accounts or net-new prospects that are hiring in Germany, Austria, or Switzerland & even if the companies are headquartered elsewhere.

Polytomic enables dynamic filtering by job location and continuous syncing of these expansion signals into the CRM.

This allows the GTM team to prioritize outreach to accounts actively expanding into target regions.

4. Surface High-Intent Accounts in Product Categories

A cybersecurity firm can monitor job descriptions for keywords like “SOC2,” “Zero Trust,” or “compliance.”

With PredictLeads, these keyword-based filters can be applied at the job posting level. Polytomic can then transform this insight into CRM data fields and automatically assign these companies to tailored marketing or outbound workflows.

How It Works

  1. Ingest PredictLeads data into Polytomic: Use Polytomic’s UI or API to import PredictLeads datasets, including Jobs, Technologies, News Events, or other signals.
  2. Filter and enrich: Apply filters based on department, location, job title, or keywords. Combine with internal firmographic or historical data.
  3. Sync to your CRM or tool stack: Polytomic allows you to push data to HubSpot, Salesforce, Google Sheets, and many other tools (no code required.)
  4. Activate GTM workflows: Enable automated lead scoring, list assignment, alerts, or outbound triggers based on fresh buying signals.

Bottom Line?

This integration bridges the gap between rich external data and actionable CRM workflows. With PredictLeads and Polytomic, go-to-market teams can:

  • Shorten the time from signal to action
  • Prioritize accounts based on real-time hiring intent
  • Reduce reliance on internal engineering resources
  • Improve campaign targeting and SDR productivity

If your team is already using PredictLeads (or considering it) and wants to enable more automated, intelligent GTM workflows, integrating via Polytomic is a fast and scalable option.

To learn more about setting up the integration, reach out to our team at PredictLeads or visit polytomic.com.

US-China Tariffs and Shopify Adoption: Signals to Watch

Trade tensions between the US and China are once again front and center — and this time, the numbers are steep, affecting hiring signals in various sectors.

  • China’s finance ministry has announced an 84% tariff on all goods imported from the US.
  • In response, the US has implemented a 104% tariff on all Chinese goods, which officially took effect today, Wednesday, April 9.

While it remains to be seen whether a last-minute deal will be struck, if these tariffs go into effect as planned, they are expected to introduce significant friction into global ecommerce, logistics, and retail operations, influencing hiring signals in these industries.

At PredictLeads, we’re looking into how this situation might influence two key areas where strategic shifts often show up first:

  • Hiring signals across ecommerce and logistics
  • Technology adoption patterns, particularly around Shopify

Shopify: A platform exposed to global flows

Shopify plays a central role in enabling international ecommerce expansion. It’s widely used by brands that rely on cross-border fulfillment and Chinese manufacturing, making it particularly exposed to the effects of rising tariffs, which also affects hiring signals for roles related to Shopify and ecommerce.

If the new trade restrictions take hold:

  • Some sellers may pause or delay global expansion efforts.
  • Others might shift their infrastructure strategy toward more localized platforms or hybrid solutions.
  • We may see slowed adoption of Shopify among brands operating from or targeting heavily affected markets.

Together with our partners in the market intelligence space, we’re keeping a close eye on the data — particularly around Shopify adoption trends and ecommerce tech stack changes — to better understand how and where these shifts might emerge.

It’s still early, but this is the moment to start watching for new hiring signals.

Hiring signals: A directional early warning

Job data has historically been one of the earliest and most reliable indicators of how companies react to market disruption, often seen in hiring signals.

Over the next several weeks, we’ll be tracking:

  • New job postings that mention Shopify, global logistics, or cross-border ecommerce
  • Changes in hiring behavior tied to international expansion roles
  • Increased focus on domestic operations, regional warehousing and job creations, and supply chain resilience

These subtle shifts in hiring priorities can offer a first glimpse into how companies are adjusting their ecommerce strategies in response to the tariffs.

For market intelligence teams: where to focus

Whether you’re analyzing ecommerce growth, tracking tech adoption, or assessing exposure to global supply chain risk, now is the time to monitor alternative data sources more closely for new signals related to hiring.

We recommend focusing on:

  • Tech stack detections — to identify the adoption slowdown at platforms like Shopify
  • Hiring data — to spot where expansion plans are being paused or redirected due to new hiring signals
  • Regional trends — to see whether companies begin shifting focus toward LATAM, Southeast Asia, or domestic-only models

These early indicators can inform broader trend analysis well before public earnings or analyst reports reveal the full picture.

Stay ahead of the shift

As of April 9, the tariffs are now in effect — and unless there’s a breakthrough soon, the ripple effects across global trade could intensify, signaling new hiring patterns.

If you’re preparing internal research, building trend reports, or want a deeper look into Shopify adoption and ecommerce hiring trends in this context, feel free to reach out. We’re happy to share additional cuts of the data or collaborate on deeper analysis.

This is a developing story, and the signals are just starting to surface.

How Experts Use PredictLeads Data to Drive Smarter Outreach & Growth 🤔

To enhance your sales strategy, consider using PredictLeads data for your outreach. The best sales and marketing teams know that data is the foundation of relevance. Whether you’re crafting hyper-personalized outreach, identifying high-intent leads, or building a smarter go-to-market strategy, having the right insights at the right time makes all the difference.

At PredictLeads, we’re excited to see industry leaders leveraging our data to build more efficient, scalable, and highly relevant outreach strategies. Recently, some of the best in B2B sales, GTM, and demand generation have shared how PredictLeads enhances their workflows – and we want to highlight their incredible insights.

How Experts Are Using PredictLeads data for sales outreach

Across LinkedIn, industry professionals have been tagging PredictLeads and showcasing real-world applications of ourJob Openings, Technographic and News Events dataset.

📌 Job Openings as a Sales Trigger

🔹Soheil Saeidmehr (ColdIQ) and Dan Rosenthal (ColdIQ) incorporate job data into ABM (Account-Based Marketing) strategies. By combining hiring signals with firmographic and technographic data, they’re ensuring outreach messages are laser-focused on real buyer needs.

🔹 Hermann Siering (Noord50) points out how job vacancies can be a powerful trigger for outbound sales. If a company is hiring for a marketing role, why not introduce them to marketing automation software that can help their growing team? By scraping job postings with PredictLeads, sales teams can identify high-intent prospects before competitors do.

🔹 Davidson B (Zerocac) takes this further by highlighting how 57+ sales triggers, including hiring data, can boost GTM efficiency. If your sales team is still relying on manual research, you’re missing out on automated intent signals that help you reach the right accounts at the right time.

📌 Technographic Data for Smarter Targeting

🔹 Michel Lieben (ColdIQ) recognizes that B2B data is evolving, and relying on traditional databases isn’t enough. Instead, companies are turning to PredictLeads for real-time technographic insights, helping them find companies that use specific tools.

🔹 Andreas Wernicke (Snowball Consult) howcases PredictLeads, emphasizing how deep tech stack insights can determine whether a prospect is a good fit before outreach even starts.

🔹 Eric Nowoslawski (Growth Engine X) explains how technographic data can be used not just for competitor switching campaigns, but also for identifying complementary integrations. If a company already uses a relevant tool, your solution may be a perfect fit for their existing stack.

📌 Combining Multiple Signals for High-Intent Outreach

🔹 Dvin Malekian (Warmleads.io) and Elom Maurice A. stress the importance of layering multiple signals – technographic data, hiring patterns, and company news – to build hyper-targeted outreach lists. With PredictLeads, sales teams can enrich data without manually cross-referencing multiple sources.

🔹 Benoit Lecureur (gyfti) and Papa A. Sefa (Leveraged Outbound) highlight PredictLeads as a core provider of raw intent data, which can then be enhanced through tools like Clay and Smartlead for fully automated campaigns.

🔹 Hammad Afzal (Netsol Technologies) incorporates PredictLeads into a 2025-ready GTM stack, using our data to identify high-intent accounts and track job changes that indicate buying readiness.

📊 Why PredictLeads Data Gives You an Edge

Traditional cold outreach is a numbers game – but without the right insights, it’s just noise. Instead of blindly messaging tens of thousands of prospects, top-performing teams use data to turn cold emails into highly targeted, relevant outreach.

With Hiring signals, Technographic insights, and News Events data, teams can:

Reach the right accounts at the right time based on real buying signals
Personalize at scale without sacrificing efficiency
Cut through the noise by focusing on companies that actually need their solution

Cold outreach isn’t the problem  – irrelevant outreach is. PredictLeads helps you change that.

THANK YOU! 🙏 💜

We’re incredibly grateful to all the content creators and industry experts who have shared how they use our data. There are many more insights out there, and we’d love to feature even more strategies!

💡 Have you used PredictLeads in your sales or marketing process? Drop your experience in the comments or tag us on LinkedIn – we’d love to hear from you!

#B2BData #SalesIntelligence #GrowthMarketing #SalesEnablement #OutboundProspecting #ABM #GTM

Unlock Business Insights with the Clay + PredictLeads Integration

The Clay and PredictLeads integration is a game-changer for businesses looking to supercharge their prospecting and enrichment capabilities. This integration enables users to access real-time data about companies, including the latest news, hiring trends, partnerships, and tech stack insights – all within the Clay platform. Whether you’re a sales professional, a recruiter, or an investor, this integration gives you the tools to make data-driven decisions and take actionable steps.

Here’s a step-by-step guide to get started with the integration involving Clay plus PredictLeads technology:

Step 1: Register at PredictLeads

To begin, create your PredictLeads account by signing up here. Upon registration, you’ll receive 100 free API calls per month – perfect for getting started with this great Clay + PredictLeads connection.

  • Once your account is set up, navigate to your Dashboard to locate your API Key and API Token.
  • Keep these credentials handy since they’ll be essential for connecting PredictLeads to Clay.

💡 Need more API calls? Reach out to PredictLeads at info@predictleads.com or use this link

Step 2: Add PredictLeads to Clay

Now that you have your API credentials, it’s time for the integration between Clay and PredictLeads to enhance your data handling.

  1. Open Clay and head to Settings > Connections.
  2. In the integration provider search panel, look for PredictLeads.
  3. Click Add Connection.

You’ll be prompted to:

  • Name your connection: Choose a descriptive name for your key.
  • Enter your PredictLeads API credentials: Use your API Key as the username and API Token as the password.

Once saved, Clay will generate a secure PredictLeads connection for you. 🎉

Step 3: Create a Workspace in Clay

With PredictLeads now connected, it’s time to build your workspace and start utilizing the integration features with Clay and PredictLeads tools.

  1. Create a new workspace in Clay – > this is where you’ll manage the domains you want to enrich.
  2. You can either:
    • Import domains directly from your computer or CRM, or
    • Search for companies directly within Clay.
    • And more… It’s Clay, so you know they got you covered 😉

Step 4: Enrich Your Data with PredictLeads

Once your domains are added, it’s time to enrich them using PredictLeads’ datasets, an essential part of the Clay and PredictLeads setup.

  1. Select the domains you want to enrich.
  2. Search for PredictLeads in the enrichment panel.
  3. Choose the datasets that suit your needs:
    • Find Most Recent News: Stay updated on product launches, funding rounds, or acquisitions.
    • Analyze Tech Stack: Gain insights into a company’s frequently mentioned technologies.
    • Find Open Jobs: Uncover hiring trends and identify growth areas.
    • Find Connections: Discover vendors, customers, and investors linked to a company.
  1. Configure your inputs and let PredictLeads do the magic.

Managing API Calls

Each enrichment will consume PredictLeads API calls, so keep an eye on your usage here

For additional API capacity, contact PredictLeads at info@predictleads.com.

Why Use the Clay + PredictLeads Integration?

This integration streamlines the process of gathering actionable insights. With just a few clicks, you can harness the power of connecting Clay and PredictLeads together to:

  • Personalize your outreach with relevant news.
  • Stay ahead of competitors by analyzing hiring trends and tech stacks.
  • Strengthen pitches with verified customer or vendor connections.

Whether you’re looking to close deals faster, identify investment opportunities, or build stronger partnerships, the Clay + PredictLeads system is your ultimate tool.

🎯 Ready to try it out? Start by registering at PredictLeads and connecting it to your Clay account. Let us know if you have any questions – We’re happy to help 💜 💪

Happy enrichments! 🚀

Boost Your Lead Generation and Email Campaigns with Connections Dataset

Hey everyone! Today, let’s dive into how personalized sales outreach with data can revolutionize your approach and make connections more meaningful.

In sales, finding and engaging the right prospects can feel like searching for a needle in a haystack. Sending non personalized emails is just a thing of the past and companies offering sales solutions are looking into data to add that personalized touch that increases those reply rates that we all like.

Job Openings Dataset as well as the News Events Dataset are incredibly useful and widely adopted for uncovering new leads and improving sales outreach. However, there is a unique dataset that is gaining significant attention. This dataset, which is not yet widely used due to its limited availability, holds great potential for transforming sales strategies. Here is why:

We all know that companies like to put logos of other companies they work with, on their website to gain credibility. Since those logos are often not backlinked, PredictLeads has built an image recognition system that connects these logos with company domain names. By checking the company’s Case studies pages, testimonials, “Our customers” sections and more allows PredictLeads systems to identify them as customers, partners, vendors, sponsors and more.


Here’s a quick rundown of how the Connections Dataset can revolutionize your sales efforts and how it’s used to target High-Value Prospects.

Identifying and Prioritizing Key Prospects

First up, let’s talk about finding those high-value prospects. With the Connections Dataset, you can pinpoint companies that already have significant relationships with your existing clients or partners. This means they’re more likely to convert because there’s already some trust and relevance built in.

How to Do It:

  1. Analyze Data: Dive into the Connections Dataset to find companies that share multiple connections with your current network.
  2. Prioritize Prospects: Rank these companies based on the number and quality of shared connections.
  3. Sales Outreach: Focus your efforts on these high-value prospects. Make sure to highlight the mutual connections and the benefits of joining an established network.

Example: A SaaS company finds that several of its clients are partners with a leading industry player. By targeting this player and emphasizing the mutual benefits, they can craft a top notch outreach that’s hard to ignore.

Next, let’s make your emails shine

Personalized outreach campaigns are the way to go because they address the specific needs of each recipient. By referencing the target company’s partnerships or integrations, your emails can be way more relevant and engaging.

How to Do It:

  1. Gather Insights: Use the Connections Dataset to get detailed insights into the target company’s partnerships and integrations.
  2. Personalize Emails: Craft email content that references these relationships, making it super relevant.
  3. Automate Personalization: Use AI tools to scale this personalization process, ensuring each email is tailored to the recipient’s context.

Example: An AI-powered email platform identifies a potential client’s recent partnership with an e-commerce platform like Shopify. They send a personalized email campaign highlighting success stories of similar clients who benefited from this integration. Boom -> relevance and appeal.

Warm Introductions through Mutual Connections

Finally, let’s talk about using mutual connections for warm introductions. These can significantly boost your chances of successful engagement. The Connections Dataset can help you leverage existing relationships to approach leads with more trust and credibility.

How to Do It:

  1. Map Networks: Use the ConnectionsDataset to map out mutual connections between your company and target leads.
  2. Request Introductions: Reach out to these mutual connections for warm introductions, explaining the mutual benefits.
  3. Follow-Up Strategy: Develop a follow-up strategy that leverages the credibility of the mutual connection.

Example: A lead generation company finds that one of its key clients is also a partner of a high-value prospect. They request an introduction from the key client, who provides a warm referral, significantly improving engagement chances and improves their data-driven sales outreach.

Utilizing AI for Enhanced Personalization & amplify the Impact with automation

AI can take your use of the ConnectionsDataset to the next level by automating the analysis and personalization processes. Here are some tips: 

  1. Automated Analysis: AI analyzes the dataset to identify patterns and insights, like high-value prospects and mutual connections.
  2. Scale Personalization: AI personalizes email content at scale by incorporating insights from the dataset into email templates.
  3. Predictive Analytics: AI uses historical data to predict which prospects are most likely to convert, helping prioritize efforts.
  4. Continuous Learning: AI systems learn from campaign outcomes, refining algorithms to improve future personalization and targeting.

Example Implementation:

An AI-powered email platform integrates with the Connections Dataset, analyzing the dataset to identify key relationships and generating personalized email content. It predicts which prospects will respond positively and continuously refines its personalization algorithms.

Conclusion

Since 2019, over 170 million business connections have been detected, with business connections data available for 38,5 million websites. Last month alone, there were approximately 12 million business connections, and around 57 million over the past year. The Connections Dataset is a goldmine for lead generation companies and those using AI for personalized emails. By providing detailed insights into company relationships, it helps you target high-value prospects, create relevant and engaging email campaigns, and leverage mutual connections for credible engagements. Combined with AI, it automates these processes and achieves personalization at scale, leading to higher engagement rates and better sales outcomes.

Feel free to let us know if you or if you’d like to learn more. We’re here to help:)!

AI Adoption and Sector Shifts Through Job Openings Data

Artificial intelligence is changing the job market, prompting significant shifts in workforce needs across various sectors. By analyzing job postings, investment companies can gain insights into which industries are reducing their hiring for roles likely to be automated. This helps them understand potential revenue impacts and growth opportunities.

AI tools are increasingly integrated into business functions, ranging from data analysis to customer service and legal assistance. For example, paralegals, traditionally performing research and document review, are being replaced by AI systems. These systems can quickly and accurately handle these tasks. This trend is highlighted in Nexford University’s article “How Will Artificial Intelligence Affect Jobs 2024-2030,” which underscores the growing use of AI in roles previously performed by humans. Monitoring job postings can reveal decreases in hiring for such roles, indicating a shift towards AI-driven solutions.

Strategic Insights for Investment

Investment companies must stay ahead of market changes to make informed decisions. A decline in job openings for traditional roles, such as customer service representatives or paralegals, in sectors like customer service, sales, and legal services can signal a move towards AI automation. This information is crucial for identifying industries at risk of revenue loss due to a lack of automation foresight. It helps investors focus on more promising areas.

For example, companies like Google and Duolingo are already replacing human roles with AI technologies. Google has integrated AI into its customer care and ad sales processes. Meanwhile, Duolingo uses AI for content translation, reducing the need for human contractors.

Economic Impact of AI

The economic implications of AI are substantial. A McKinsey report predicts that AI could add $13 trillion to global economic activity by 2030, primarily through labor substitution and increased innovation. However, this growth comes with job displacement. Monitoring job opening trends helps investment firms gauge which companies and sectors are reducing their workforce due to AI, identifying potential risks and opportunities.

Recent examples include:

Understanding AI adoption through job postings allows investment companies to anticipate market shifts. They can focus on high-growth sectors. Sectors such as AI development, advanced manufacturing, and healthcare innovation are likely to attract more investment. This is due to their proactive adoption of AI technologies. This foresight helps investors mitigate risks and capitalize on new growth opportunities.

Additional Data from the ADP National Employment Report

The ADP National Employment Report for June 2024 provides a comprehensive overview of job trends. According to the report, private employers added 150,000 jobs in June, marking a slowdown in job creation for the third straight month. “Job growth has been solid, but not broad-based. Had it not been for a rebound in hiring in leisure and hospitality, June would have been a downbeat month,” said Nela Richardson, Chief Economist at ADP​ (ADP Media Center)​.

This data underscores the importance of monitoring employment trends to understand the broader economic impact of AI. It informs strategic investment decisions.

The chart titled “ADP Employment: Establishment Size Year-over-Year Percent Change” tracks the year-over-year percentage change in employment across different establishment sizes from 2011 to 2024. 

Here are some key points:

  • Trend Analysis: The chart illustrates fluctuations in employment growth across different establishment sizes over the years. A notable drop is observed around 2020, corresponding with the COVID-19 pandemic’s impact on employment. Post-2020, there is a marked recovery, with larger establishments (500+ employees) showing a more robust recovery compared to smaller establishments.
  • Recent Trends: As of June 2024, the growth rates have stabilized. However, smaller establishments (1-19 employees) show slower growth compared to larger establishments. This indicates that larger companies are recovering and possibly investing more in automation and AI technologies. Meanwhile, smaller businesses are facing more challenges.

This chart helps visualize the employment dynamics and how different-sized businesses have been affected over the years. It provides valuable context for understanding the broader economic landscape and the impact of AI on employment.

For more detailed insights and statistics, the full ADP Employment Report is available here.

Conclusion

By analyzing job openings data, investment companies can gain valuable insights into AI adoption trends and their impact on various sectors. This approach helps identify industries reducing traditional roles due to AI. It enables better-informed investment decisions. Utilizing datasets like those from PredictLeads can provide the detailed, real-time insights needed to stay ahead of market shifts. This helps mitigate risks and seize growth opportunities in an AI-driven economy.

  • Job Openings Data: Since 2018, there have been 166 million job openings detected.
  • Data Availability: Job openings data is available for 1.6 million websites.
  • Recent Trends: Last month, there were 5 million job openings. Over the past year, approximately 50 million job openings were recorded globally.
  • Active Job Openings: Currently, there are about 7 million active job openings uncovered by PredictLeads.

These statistics underscore the vast amount of data available to track AI adoption and its effects on the job market. They provide investment firms with the necessary tools to make informed decisions.

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